Optimize AI Agent Costs Automatically

Zero audits your agent runs, classifies tasks by complexity, and recommends model switches that cut spending without sacrificing output quality.

Zero connects:

What Zero delivers

Optimize AI Agent Costs Automatically — sample output from Zero — screenshot 1

What the problem is

End of month. The AI infrastructure bill lands: $17K this month, up from $12K last month. You dig in and find that the daily tech debt scan - which runs a few grep scripts and files GitHub issues - is using Claude Opus. The merge queue monitor, which checks if CI is green and posts to Slack, is also on Opus. Neither task needs anything close to Opus. You could audit every schedule manually, or you could ask Zero to classify each task by complexity and recommend which ones to downgrade.

How Zero fixes it

Step 1: Connect your tools

vm0

Required

vm0 - provides access to agent run logs, schedule configurations, and model billing data. Zero uses this to analyze what each task does and what it costs.

Connect

Slack

Required

Slack - delivers the cost optimization report to your engineering or dev channel.

Connect

Step 2: Ask Zero

@Zero audit all agent schedules and runs. Classify each task as low, medium, or high complexity based on the actual work done. Recommend which tasks can safely switch to a cheaper model without quality loss. Post the report to Slack.

try it

Zero audits all agent runs and token usage

Zero queries your agent run logs, examines what each task actually does - how many turns, what tools it calls, how complex the reasoning is - and calculates the current cost per task.

Zero classifies tasks by complexity tier

Zero sorts tasks into three buckets: low complexity (read-and-summarize, grep-and-post), medium complexity (multi-source aggregation, structured analysis), and high complexity (code generation, open-ended reasoning). Each tier gets a recommended model.

Zero posts actionable recommendations with savings estimates

The cost audit lands in Slack with a clear table: current model, recommended model, and estimated savings per task. Zero flags which switches are safe to make immediately and which need a trial period to verify quality.

Step 3: Take it further

Switch a low-risk task to a cheaper model

Start with the safest recommendation and verify quality holds.

@Zero switch the merge-queue-monitor schedule to use GLM-5.1 instead of Sonnet

try it

Run a comparison test

Run the same task on both models and compare outputs before committing.

@Zero run the tech-debt-scan prompt on both Opus and GLM-5.1, then compare the results side by side

try it

Make it routine

Schedule weekly cost audits so spending never creeps up unnoticed.

@Zero every Monday at 9am, audit agent costs and post optimization recommendations to #dev

try it

Tips for better results

●Start with low-risk tasks - monitoring, notifications, and daily summaries are safe to downgrade first. Code generation and open-ended reasoning should be last.

●Track quality metrics before and after each switch. If error-triage-daily starts missing issues after a model change, revert immediately.

●Review cost reports weekly, not monthly - small leaks compound fast, and a weekly cadence catches problems before the bill arrives.